Summary of Recovering Labels From Local Updates in Federated Learning, by Huancheng Chen and Haris Vikalo
Recovering Labels from Local Updates in Federated Learning
by Huancheng Chen, Haris Vikalo
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel label recovery scheme called Recovering Labels from Local Updates (RLU) to address gradient inversion (GI) attacks in federated learning (FL). RLU achieves near-perfect accuracy when attacking untrained models and high performance even in realistic settings where clients run multiple local epochs, train on heterogeneous data, and use various optimizers. The method estimates labels by solving a least-square problem analyzing the correlation between label and update of the output layer. Experimental results on several datasets, architectures, and data heterogeneity scenarios demonstrate that RLU outperforms existing baselines in terms of PSNR and LPIPS for reconstructed images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep our personal data safe by developing a new way to recover labels from model updates in federated learning. This is important because some hackers might try to figure out what we’re doing based on the tiny pieces of information they get from us. The team came up with a clever method that can do this really well, even when things are complex and different parts are working together in strange ways. They tested it on many different cases and showed that it’s way better than other methods at keeping our data safe. |
Keywords
» Artificial intelligence » Federated learning
